The LandTrendr (LT) algorithm has been used widely for analysis of change in Landsat spectral time series data, but requires significant pre-processing, data management, and computational resources, and is only accessible to the community in a proprietary programming language (IDL). Here, we introduce LT for the Google Earth Engine (GEE) platform. The GEE platform simplifies pre-processing steps, allowing focus on the translation of the core temporal segmentation algorithm. Temporal segmentation involved a series of repeated random access calls to each pixel’s time series, resulting in a set of breakpoints (“vertices”) that bound straight-line segments. The translation of the algorithm into GEE included both transliteration and code analysis, resulting in improvement and logic error fixes. At six study areas representing diverse land cover types across the U.S., we conducted a direct comparison of the new LT-GEE code against the heritage code (LT-IDL). The algorithms agreed in most cases, and where disagreements occurred, they were largely attributable to logic error fixes in the code translation process. The practical impact of these changes is minimal, as shown by an example of forest disturbance mapping. We conclude that the LT-GEE algorithm represents a faithful translation of the LT code into a platform easily accessible by the broader user community.

Disturbance is a critical ecological process in forested systems, and disturbance maps are important for understanding forest dynamics. Landsat data are a key remote sensing dataset for monitoring forest disturbance and there recently has been major growth in the development of disturbance mapping algorithms. Many of these algorithms take advantage of the high temporal data volume to mine subtle signals in Landsat time series, but as those signals become subtler, they are more likely to be mixed with noise in Landsat data. This study examines the similarity among seven different algorithms in their ability to map the full range of magnitudes of forest disturbance over six different Landsat scenes distributed across the conterminous US. The maps agreed very well in terms of the amount of undisturbed forest over time; however, for the ~30% of forest mapped as disturbed in a given year by at least one algorithm, there was little agreement about which pixels were affected. Algorithms that targeted higher-magnitude disturbances exhibited higher omission errors but lower commission errors than those targeting a broader range of disturbance magnitudes. These results suggest that a user of any given forest disturbance map should understand the map’s strengths and weaknesses (in terms of omission and commission error rates), with respect to the disturbance targets of interest.

Remote detection of forest disturbance remains a key area of interest for scientists and land managers. Subtle disturbances such as drought, disease, insect activity, and thinning harvests have a significant impact on carbon budgeting and forest productivity, but current change detection algorithms struggle to accurately identify them, especially over decadal timeframes. We introduce an algorithm called Edyn, which inputs a time series of residuals from harmonic regression into a control chart to signal low-magnitude, consistent deviations from the curve as disturbances. After signaling, Edyn retrains a new baseline curve. We compared Edyn with its parent algorithm (EWMACD—Exponentially Weighted Moving Average Change Detection) on over 3500 visually interpreted Landsat pixels from across the contiguous USA, with reference data for timing and type of disturbance. For disturbed forested pixels, Edyn had a mean per-pixel commission error of 31.1% and omission error of 70.0%, while commission and omission errors for EWMACD were 39.9% and 65.2%, respectively. Edyn had significantly less overall error than EWMACD (F₁ = 0.19 versus F₁ = 0.13). These patterns generally held for all of the reference data, including a direct comparison to other contemporary change detection algorithms, wherein Edyn and EWMACD were found to have lower omission error rates for a category of subtle changes over long periods.

Disturbance is a key influence on forest carbon dynamics, but the complexity of spatial and temporal patterns in forest disturbance makes it difficult to quantify their impacts on carbon flux over broad spatial domains. Here we used a time series of Landsat remote sensing images and a climate-driven carbon cycle process model to evaluate carbon fluxes at the ecoregion scale in western Oregon. Thirteen percent of total forest area in the West Cascades ecoregion was disturbed during the reference interval (1991-2010). The disturbance regime was dominated by harvesting (59 % of all area disturbed), with lower levels of fire (23 %), and pest/pathogen mortality (18 %). Ecoregion total Net Ecosystem Production was positive (a carbon sink) in all years, with greater carbon uptake in relatively cool years. Localized carbon source areas were associated with recent harvests and fire. Net Ecosystem Exchange (including direct fire emissions) showed greater interannual variation and became negative (a source) in the highest fire years. Net Ecosystem Carbon Balance (i.e. change in carbon stocks) was more positive on public that private forestland, because of a lower disturbance rate, and more positive in the decade of the 1990s than in the warmer and drier 2000s because of lower net ecosystem production and higher direct fire emissions in the 2000s. Despite recurrent disturbances, the West Cascades ecoregion has maintained a positive carbon balance in recent decades. The high degree of spatial and temporal resolution in these simulations permits improved attribution of regional carbon sources and sinks.